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- Title
Research on a Task Offloading Strategy for the Internet of Vehicles Based on Reinforcement Learning.
- Authors
Xiao, Shuo; Wang, Shengzhi; Zhuang, Jiayu; Wang, Tianyu; Liu, Jiajia
- Abstract
Today, vehicles are increasingly being connected to the Internet of Things, which enables them to obtain high-quality services. However, the numerous vehicular applications and time-varying network status make it challenging for onboard terminals to achieve efficient computing. Therefore, based on a three-stage model of local-edge clouds and reinforcement learning, we propose a task offloading algorithm for the Internet of Vehicles (IoV). First, we establish communication methods between vehicles and their cost functions. In addition, according to the real-time state of vehicles, we analyze their computing requirements and the price function. Finally, we propose an experience-driven offloading strategy based on multi-agent reinforcement learning. The simulation results show that the algorithm increases the probability of success for the task and achieves a balance between the task vehicle delay, expenditure, task vehicle utility and service vehicle utility under various constraints.
- Subjects
INTERNET strategy; TIME-varying networks; ALGORITHMS; REINFORCEMENT learning; COST functions; INTERNET of things; VEHICULAR ad hoc networks
- Publication
Sensors (14248220), 2021, Vol 21, Issue 18, p6058
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s21186058